Modified QDMC Based on Instantaneous Linearization of Neural Network Models in Nonlinear Chemical Processes.

Abstract

Dynamic matrix control (DMC) has been widely applied in many industrial processes due to the simple design for multivariable process control. However, lack of the adaptability restricts its application in the highly nonlinear and complex processes. The nonlinear model predictive controller for processes is needed, but, from a computational perspective, it has quite comprehensive demands. In this paper, modified quadratic dynamic matrix control (MQDMC), which integrates DMC with the neural network model, is proposed. The predictive model control strategy linearizes the model by applying instantaneous linearization to the nonlinear neural network model at each sampling time. MQDMC has two advantages. First, less computation of linear DMC is used. Second, the nonlinear characteristics of neural networks can be incorporated into predictive control design. In the simulation studies, the performance of MQDMC matches that of nonlinear neural network model predictive control.